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- import sklearn
- file = open('train.txt', 'r')
- text = file.read()
- result = []
- for line in text.splitlines():
- result.append(list(line.split()))
- X=[]
- Y=[]
- for value in result:
- X.append(list(map(float, value[:176])))
- Y.append(value[176])
- Y = [float(y) for y in Y]
- Z=[]
- testFile=open('dev_no_label.txt')
- text = testFile.read()
- result=[]
- for line in text.splitlines():
- result.append(list(line.split()))
- for value in result:
- Z.append(list(map(float, value)))
- def save(predictions):
- outFile = open('predictions.txt', 'w')
- for y in predictions:
- print(int(y), file=outFile)
- outFile.close()
- from sklearn import svm, cross_validation, tree, linear_model
- X_train,X_test,y_train,y_test=cross_validation.train_test_split(X,Y,test_size=0.4, random_state=0)
- from sklearn.cross_validation import train_test_split
- from sklearn.preprocessing import StandardScaler
- from sklearn.datasets import make_moons, make_circles, make_classification
- from sklearn.neighbors import KNeighborsClassifier
- from sklearn.svm import SVC
- from sklearn.tree import DecisionTreeClassifier
- from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
- from sklearn.naive_bayes import GaussianNB
- from sklearn.lda import LDA
- from sklearn.qda import QDA
- from sklearn.pipeline import Pipeline
- from sklearn.svm import LinearSVC
- def tryAll():
- classifiers = [
- KNeighborsClassifier(3),
- DecisionTreeClassifier(max_depth=5),
- RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),
- AdaBoostClassifier(),
- GaussianNB(),
- LDA(),
- QDA()]
- for clf in classifiers:
- clf=clf.fit(X_train, y_train)
- print(clf.score(X_test,y_test), clf)
- clf=Pipeline([('feature_selection', LinearSVC(penalty='l1', dual=False)),('classification', RandomForestClassifier())])
- clf.fit(X_train,y_train)
- print(clf.score(X_test,y_test), clf)
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